Biomedical engineering


Active interdepartmental participation of the biomedical engineering technician (BMET) with clinicians is an opportunity to reduce systemic events guided by empirical evidence that 1) establishes adverse events with medical equipment and 2) associates nursing effectiveness with access to functioning equipment. Though prior research has documented interdependency in nurse-physician relationships (and in such non-clinical health support services as laboratory and pharmaceutical departments), few studies in mainstream literature on quality have related medical professional interdependencies to the BMET. The promotion of National Patient Safety Goals, federal legislation (the Safe Device Act of 1990), and recommendations from agencies— The Joint Commission and the United States Center for Disease Control and Prevention (CDC), all point to a multidisciplinary approach for detecting and resolving systemic problems. Therefore, comprehending the interdependent role of the BMET in hospital care is important for reducing persistent problems like Nosocomial Infections (NI) and other adverse systemic events that affect clinical outcomes. Industry research documents the positive contributions of BMET professional integration into facility management in Management Information Systems (MIS), and empirical evidence has shown that their professional contributions influence nursing performance and thus, patient outcomes. Yet, BMET integration to departments like Infection Control and Central Sterile where BMETs' specific knowledge of medical equipment can apply directly is rare, if not entirely absent. Delaying such professional integration can hamper effective response to offset the Centers for Medicare and Medicaid (CMS) payment reductions that went into effect on October 1, 2008. The CMS denies payment for treatment of infections it deems 'preventable' by proper interdependent precautions. Infections already under scrutiny as preventable include mediastenitis, urinary tract infections, and catheter-related blood stream infections. Furthermore, formal Medicare Conditions of Participation (CoP) now require hospitals to develop initiatives to reduce medical errors by identifying and addressing threats to patient safety. In both these challenges the medical equipment used in clinical care can adversely affect patient outcomes. Clearly, the health care system must tackle the common healthcare associated infections (HAI) just mentioned as well as others that may be added to the CMS list, or face overwhelming financial costs. Understanding the BMET professional relationship with nursing, given the structural and process considerations of the level of quality (LOQ) as measured by Clinical Effectiveness, Clinical Efficiency, and Regulatory Compliance, will be essential for meeting this challenge. This study's extensive literature review led to the development of a conceptual hypothesized model based on Donabedian's 1988 Triad of Structure, Process, and Outcome and fused with Integrated Empirical Ethics as a foundation for BMET professional interdependency and for consolidated attack on adverse systemic events. This theoretical integration has the potential to advance quality of clinical care by illuminating the factors directly or indirectly influencing patient outcomes. Primary data were gathered through the Biomedical Engineering Interdepartmental Survey that collected BMETs' professional perceptions of organizational factors (Structural Complexity), process factors (Process Adequacy), and Level of Quality and Control variables yielding information about the individual respondents and the facilities where they work. The unit of analysis in this study is the biomedical engineering technician functioning in hospital support services to ensure patient safety and quality of care. Initial survey results underwent data cleansing to eliminate the impact of missing items. Next, Confirmatory Factor Analysis applied to the survey data determined the construct validity and reliability of the measurement instrument. Statistically tested regression models identified structure and process factors that may affect the LOQ in terms of systemic adverse events and lack of compliance. The statistical analysis and assumption tests that confirm internal validity infer that hospital Level of Quality is significantly influenced at R2 = 88.1% by Structural Complexity. The combined measurement model and models for each latent construct achieved Cronbach α results > 0.7, indicating internal reliability of the Biomedical Engineering Interdepartmental (BEI) survey instrument. The final measurement models of the latent constructs—structural complexity (six factors), process adequacy (five factors), and level of quality (six factors) are correlated and significant at t > 1.96, p < .001 (2-tailed). The Structural Equation Model without controls are correlated and significant at t > 1.96 on all factors, indicating an approximate standard distribution at p < .001 level (2-tailed). Goodness of fit model analysis findings indicates that the models reasonably fit the data. The largest correlation is expressed between structural complexity and process adequacy (0.217 to 0.461), p=.01 (2-tailed). Respondent and facility control variables added to the Structural Equation Model are correlated with low impact but not statistically significant. The findings have implications for theory, methodology, external policy, and internal hospital administrative management. The theoretical contributions of the study include the instrument development, measurement models, and the Structural Equation Model for hospital level of quality. The statistical analysis of the relationships of Donabedian's Triad indicates that both structural complexity and process adequacy are explanatory for the outcome variable of level of quality. Several statistically significant predictors of quality support an integrated approach to systemic problems. They are Uniform Standards, Inter-Professional Training, Coordination Evidence, Interdepartmental Work and Device Failure Recognition. Moreover, the application of Integrated Empirical Ethics provides a foundation for management resolution that can improve the hospital level of quality by consolidating divergent internal and external controls by providing implementation guidance to overcome medical plurality as empirical evidence continues to emerge. The study defines the outcome measures of Quality—Effectiveness, Efficiency, and Regulatory Compliance in the context of Clinical Engineering. The study results suggest pertinent external policy recommendations, foremost of which arises from the responses to the item concerning Uniform Standards: "Standards are applied equally across all departments." In the BMET community, only about 20 per cent strongly agree with this statement; approximately 33 per cent agree. Because of divergent ethical and national regulatory policies applied to professional affiliations rather than the medical community at large, a policy adapting regulatory initiatives having the same focus on patient outcomes (e.g., CMS CoP; National Patient Safety Goals) would generate the best initiatives for reducing systemic adverse events and policy conflicts. Finally, results suggest that internal hospital administrators can improve the level of quality through internal process changes, in particular by addressing the process adequacy factor of Regular Meetings for the survey item: "Nursing and biomedical engineering conduct regularly scheduled meetings on equipment issues." Less than 10 per cent of the BMETs surveyed strongly agreed and about one-third agreed that this aspect of interdepartmental teamwork was accepted. The study confirms the evolution of the interdependent professional dynamic within healthcare exemplified by the combination of multiple predictors of the Level of Quality from Organizational Culture, Level of Coordination and Interdepartmental Medical Device Management. Hospital administrators can find simple, cost-effective solutions to improve clinical effectiveness (a key indicator of quality) in the components of the intervening variable of process adequacy. For example, statistical evidence shows that regular meetings between nursing and biomedical staff about equipment issues and/or linking the BMET department goals to Organization Objectives are ways to improve quality.


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Graduation Date





Wan, Thomas T. H.


Doctor of Philosophy (Ph.D.)


College of Health and Public Affairs


Public Affairs

Degree Program

Public Affairs








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Doctoral Dissertation (Open Access)


Dissertations, Academic -- Health and Public Affairs, Health and Public Affairs -- Dissertations, Academic